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Evaluating Deep Learning Models in 10 Different Languages (With Examples)

ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators — the building blocks of machine learning and deep learning models — and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. The following post is a compilation of code samples showing how to evaluate Onnx Models in 10 different programming languages.

#10 R


#9 C++


#8 Java


#7 .NET Core

Tutorial: Detect objects using an ONNX deep learning model - ML.NET

#6 Ruby


#5 Rust


#4 JavaScript


#3 Python


#2 Swift

Convert trained image classification model to iOS app via ONNX and Apple Core ML

#1 C


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About the Author

Aaron (Ari) Bornstein is an AI researcher with a passion for history, engaging with new technologies and computational medicine. As an Open Source Engineer at Microsoft’s Cloud Developer Advocacy team, he collaborates with Israeli Hi-Tech Community, to solve real world problems with game changing technologies that are then documented, open sourced, and shared with the rest of the world.

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